High-Resolution View of the Yeast Meiotic Program
Revealed by Ribosome Profiling
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Brar, G. A., M. Yassour, N. Friedman, A. Regev, N. T. Ingolia,
and J. S. Weissman. “High-Resolution View of the Yeast Meiotic
Program Revealed by Ribosome Profiling.” Science 335, no.
6068 (February 2, 2012): 552-557.
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http://dx.doi.org/10.1126/science.1215110
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Science. Author manuscript; available in PMC 2012 August 08.
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Published in final edited form as:
Science. 2012 February 3; 335(6068): 552–557. doi:10.1126/science.1215110.
High-Resolution View of the Yeast Meiotic Program Revealed by
Ribosome Profiling
Gloria A. Brar1, Moran Yassour2,3,4, Nir Friedman4,5, Aviv Regev2,3, Nicholas T. Ingolia1,†,*,
and Jonathan S. Weissman1,†
1Howard Hughes Medical Institute, Department of Cellular and Molecular Pharmacology,
University of California, San Francisco, and California Institute for Quantitative Biosciences, San
Francisco, CA 94158, USA
2Broad
Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142,
USA
3Howard
NIH-PA Author Manuscript
Hughes Medical Institute, Department of Biology, Massachusetts Institute of
Technology, Cambridge, MA 02139, USA
4School
of Engineering and Computer Science, Hebrew University, Jerusalem, 91904, Israel
5Alexander
Silberman Institute of Life Sciences, Hebrew University, Jerusalem, 91904, Israel
Abstract
Meiosis is a complex developmental process that generates haploid cells from diploid progenitors.
We measured messenger RNA (mRNA) abundance and protein production through the yeast
meiotic sporulation program and found strong, stage-specific expression for most genes, achieved
through control of both mRNA levels and translational efficiency. Monitoring of protein
production timing revealed uncharacterized recombination factors and extensive organellar
remodeling. Meiotic translation is also shifted toward noncanonical sites, including short open
reading frames (ORFs) on unannnotated transcripts and upstream regions of known transcripts
(uORFs). Ribosome occupancy at near-cognate uORFs was associated with more efficient ORF
translation; in contrast, some AUG uORFs, often exposed by regulated 5′ leader extensions, acted
competitively. This work reveals pervasive translational control in meiosis and helps to illuminate
the molecular basis of the broad restructuring of meiotic cells.
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Sexual reproduction is enabled by meiosis, a strongly conserved cell division that generates
haploid progeny from a diploid precursor. Meiosis has been studied for over a century
including extensive analyses in the budding yeast Saccharomyces cerevisiae [reviewed in (1,
2)], where it is linked to spore formation. These efforts have provided a wealth of
knowledge about the movement and changes in organization of meiotic chromosomes. Far
less is known about the molecular basis of the remodeling events that impact other aspects
of meiotic cellular physiology. Pioneering microarray studies (3) provided a basic
†
To whom correspondence should be addressed. ingolia@ciwemb.edu (N.T.I.); weissman@cmp.ucsf.edu (J.S.W.).
*Present address: Department of Embryology, Carnegie Institution for Science, Baltimore, MD 21218, USA.
Note added in proof: YDR506C and YLR445W are now named GMC1 and GMC2, respectively.
Supporting Online Material
www.sciencemag.org/cgi/content/full/science.1215110/DC1
Materials and Methods
Figs. S1 to S22
Tables S1 to S7
References (41–57)
Files S1 and S2
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framework of molecular changes accompanying yeast meiotic progression but failed to
capture many dynamic processes, in part because of extensive posttranscriptional regulation,
including specific instances of functionally significant translational control [reviewed in (2);
see also (4)]. Whether translational control plays a general role in meiotic protein
production, however, is unclear.
Ribosome profiling, based on deep sequencing of ribosome-protected mRNA fragments,
allows monitoring of translation with scale, speed, and accuracy that rivals approaches for
following mRNA levels (5, 6). Applying this method to sporulating S. cerevisiae cells
allowed us to follow the molecular events underlying meiosis with unprecedented depth.
A high-resolution atlas of meiotic mRNA abundance and new protein
synthesis
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Our studies relied on three critical features: optimized meiotic synchrony, dense time points
that oversampled meiotic transitions, and in-depth staging of each time point. We collected
samples through two separate meiosis experiments (Fig. 1, A and B, and fig. S1A). The first
used an optimized version of traditional synchronization procedures and focused on early
meiotic stages. The second time course used an estrogen-activatable variant of the Ndt80
transcription factor (4, 7), which allowed synchronous progression through the meiosis I and
II (MI and MII) chromosome segregation stages (4). Each time point was staged in detail
(Fig. 1B and figs. S2 and S3), and we selected 25 of them, chosen for comprehensive
meiotic coverage, along with two cycling vegetative samples, for ribosome profiling and
mRNA sequencing (Fig. 1A and fig. S1A). Use of time points that oversampled meiotic
stages allowed for synthesis of the data into a master time course (Fig. 1A and fig. S1B) and
selective pooling, which collapsed meiotic progression into nine categories for some
analyses (fig. S4).
Staging revealed a high degree of synchrony and provided a cytological framework to
anchor expression data (Fig. 1B and figs. S2 and S3). Examination of ribosome footprints
for specific genes showed that the sample synchrony was reflected in sharp, discrete
translation patterns (Fig. 1C). The large majority (6134 out of 6708) of genes were
translated at some point in meiosis, and most showed strong temporal regulation. In addition
to a large shift in expression patterns between vegetative cells and cells entering meiosis,
66% of meiotically expressed genes varied by at least 10-fold in protein synthesis level
through meiotic progression itself, a range that far exceeded measurement errors (Fig. 2A
and fig. S5, A to D). These changes were due largely to the meiotic program itself rather
than the nutrient deprivation conditions that accompany sporulation (fig. S6).
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Expression clustering to probe meiotic cell biology and gene function
Clustering of the time points by genome-wide protein synthesis patterns precisely
recapitulated their order (figs. S1B and S6A). Thus, dynamic control of protein synthesis
results in unique expression signatures throughout the meiotic program. Accordingly,
grouping of all genes by protein synthesis pattern through meiosis revealed numerous
multifaceted clusters (Fig. 2A).
Many clusters emerged from groups of functionally related genes. This was seen
prominently for genes involved in translation, mitochondrial function, mitochondrial
translation, nutrient uptake, proteasome function, and redox reactions (Fig. 2A, numbered in
the middle panel, and tables S1 and S2). Furthermore, a tight cluster of 27 proteins that were
synthesized at the onset of DNA replication was predominantly composed of critical DNA
replication and chromosome structure factors [Fig. 2A, top, and table S2; (8)]. Similarly,
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genes involved in recombination and synaptonemal complex (SC) formation were expressed
precisely when these processes occurred, and they emerged as a discrete group containing
46 genes from unbiased clustering of the full data set (Fig. 2A, bottom, and table S2).
Notably, this cluster included the large majority of meiotic genes with characterized roles in
double-strand–break formation, crossover-noncrossover choice, and SC structure [reviewed
in (9–11)].
Several uncharacterized genes were found in the recombination/SC cluster, which suggested
that they are involved in these intensely studied processes (1). Indeed, loss of either
YDR506C or YLR445W delayed nuclear division, consistent with a role for these factors in
prophase, when recombination and SC formation occur (Fig. 2, B and C, and fig. S7A).
ydr506cΔ and ylr445wΔ cells showed distinct, specific defects in SC morphogenesis (fig.
S7, B and C), and in both cases, the meiotic progression delay was largely alleviated when
the recombination checkpoint was bypassed by deletion of SPO11 (Fig. 2, B and C, and fig.
S7A). The strong delay in ylr445wΔ cells, however, was not fully dependent on Spo11 (Fig.
2C and fig. S7A), which implied that this gene has additional functions.
Evidence for cellular remodeling
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Whereas our ability to observe precise temporal regulation allowed specific coclustering of
some genes, there were prominent cases in which genes with a common function or
localization showed highly disparate expression patterns. For example, we found tightly
controlled but distinct patterns of expression among endoplasmic reticulum (ER) proteins,
which suggested major ER remodeling events (fig. S8 and table S3). A strong downregulation (relative to vegetative cells) of a set of ER genes, including ergosterol
biosynthesis components, occurred before meiotic induction. After meiotic entry, a broad
group of ER genes was induced, including glycosylation factors (table S3). Finally, after MI,
a subset of folding factors, sphingolipid biosynthetic genes, and trafficking components
were up-regulated. This last remodeling phase is accompanied by induction of the unfolded
protein response (UPR) (12, 13) [Fig. 1C, see also Fig. 3F below].
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Autophagy components also showed discrete patterns of expression, which suggested
dynamic control of distinct autophagic processes during sporulation [fig. S9A and table S4;
(14)]. ATG8, a gene central to many branches of autophagy (15), was highly expressed from
early in the meiotic program, and its deletion caused an early and profound meiotic defect
(fig. S9, B and C). By contrast, ATG32, a mitophagy-specific factor (16, 17), showed low
expression until the meiotic divisions (fig. S9B). Delayed onset of mitophagy may ensure
full mitochondrial function, which is needed to power early meiotic stages (18).
Consistently, atg32Δ cells progressed normally past prophase, but showed delayed meiotic
completion (fig. S9D).
Translational control in meiosis
Control of protein production reflects both regulation of mRNA levels and the efficiency
with which these messages are translated into proteins. Measuring translation rates and
mRNA levels allowed us to evaluate their relative contributions. Much transcriptional
regulation was observed, but translational control also regulated the magnitude and timing of
protein production in meiotic cells. An example of this interplay is provided by the adjacent
SPS1 and SPS2 genes (Fig. 3A). mRNA for both genes accumulated late in prophase and
persisted through the meiotic divisions, consistent with their transcriptional control by
NDT80 (19). By contrast, SPS1, but not SPS2, was strongly translationally regulated,
delaying Sps1 protein synthesis until MII (Fig. 3, A and B).
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To quantitatively evaluate the role of translational control, we calculated relative translation
efficiencies [TEs; ribosome footprint RPKM/mRNA RPKM; where RPKM is reads per
kilobase of coding sequence per million mapped reads, as in (6)] for messages across our
time course. Replicates indicated high TE reproducibility (error <20%), which allowed
sensitive measurement of dynamic translational control [fig. S5, E and F; (6)]. This
approach confirmed, both in timing and degree, the strong MI-specific translational
repression that regulates the B-type cyclin, CLB3 [(4), Fig. 3C]. At least 10 genes showed a
pattern of translational regulation highly similar to that of CLB3—including SPS1 (Fig. 3A),
GIP1, and SPO20—which, like CLB3, have known roles only late in meiosis (20–22).
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Genome-wide analysis revealed that meiotic translational regulation is both pervasive and
nuanced (Fig. 3D). As seen for vegetative cells (6), meiotic cells showed strong basal
differences in translation rates among genes (Fig. 3D). Globally, we observed a net decrease
in translation, relative to vegetative cells in their exponential growth phase, that was most
pronounced at the very earliest and latest time points (fig. S10). Further, gene-specific
regulation was widely used to dynamically tune gene expression. For example, 24% of
genes during the “core meiotic” period showed greater than threefold TE changes, a period
during which net translation capacity appears stable (fig. S10). More than 200 genes in the
full time course and 66 in the core meiotic period exhibited a dynamic range in TE that was
comparable to the ~10-fold changes seen for GCN4, an archetype of strong translational
regulation (23).
Changes in TE frequently correlated with timing of gene function (Fig. 3E). The DNA
replication factor ORC1 (24), for example, showed strong translational repression at later
meiotic stages when cells do not replicate DNA. Zip1, an SC component (25), specifically
showed poor translation in vegetative cells and spores, consistent with the lack of SC in
either state. Chitin deposition factor Rcr1 (26) is translated efficiently only at late time
points, concomitant with new cell wall generation. Finally, HAC1, the central UPR mediator
(12, 13), showed transient translational activation shortly upon transfer of cells to nutrientlimited conditions, followed by a later, stronger translational activation during the meiotic
divisions, as cells are synthesizing new membrane and spore walls (Fig. 3F). HAC1 is
regulated translationally through cytoplasmic splicing of its message (27). Consistently,
HAC1 mRNA splicing mirrored TE measurements, both in timing and degree (Fig. 3F). The
UPR has been heavily studied in yeast using harsh inhibitors of ER folding (e.g.,
dithiothreitol). This study reveals a novel physiological setting to follow UPR induction.
Noncanonical translation
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Beyond translational control of canonical open reading frames (ORFs), we also observed a
shift toward noncanonical translation during the meiotic program. Whereas vegetative cells
exhibited ~5% of ribosome footprints mapping outside annotated ORFs, in meiotic cells, up
to ~30% of footprints mapped outside of these regions (Fig. 4A). These footprints largely
mapped to discrete novel translation sites with well-defined AUG starts and stop codon
stops. We systematically annotated translation units by exploiting the strong peak in
ribosome density seen at translation initiation sites to identify utilized start codons [fig. S11;
(6)]. This strategy was sensitive and allowed de novo identification of start codons for most
known ORFs (fig. S12A) and specific, strongly enriching for ORFs initiating at AUG (fig.
S13).
Novel ORFs were found on noncanonical mRNAs, including transcripts antisense to known
ORFs, alternate transcripts at canonical loci, and transcripts in regions thought to be
intergenic (Fig. 4, B and C, and figs. S12A, S14, and S15A). We also identified instances of
genome misannotation (e.g., fig. S15B). Many newly annotated ORFs were on stable
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transcripts, similar to those predicted as noncoding in a meiotic tiling array study [(28), fig.
S14]. Our empirical strategy found translation of short ORFs (sORFs) (fig. S12A) that were
well-expressed (fig. S12, B and C) and highly regulated (fig. S16) but below the cutoff of 80
to 100 amino acids used historically to computationally identify yeast ORFs.
Recent studies have identified cellular functions for short peptides (29, 30), although the
function of these meiotic sORFs remains an open question. Minimally, our data suggest the
export of many novel transcripts into the cytoplasm, allowing translation by ribosomes.
Conversely, this data set facilitates identification of transcripts that act at the RNA level. For
example, most antisense transcripts are poorly translated, including RME2 and RME3
(antisense to IME4 and ZIP2, respectively), which are known to act through direct cistranscriptional interference of their sense counterpart [figs. S12C and S17, A, B, and D; (31,
32)]. By contrast, a transcript antisense to YFL012W that shows no transcriptional
interference activity contained prominent regions of translation [(31), fig. S17, C and D].
uORFs in meiosis
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The second major source of novel meiotic ribosome density was leader sequences
[commonly called 5′ untranslated regions (5′UTRs)], situated upstream of the canonical
ORFs (Fig. 4D and fig. S18A). We saw no general meiotic increase in footprints in 3′UTRs,
which argues against a nonspecific increase in translational background noise. Examination
of individual gene leaders revealed short footprint spans that started at either AUGs or nearcognate codons and generally spanned the region until the next stop codon (Fig. 4D and fig.
S18A). Nearly 300 of such upstream ORFs (uORFs) have been identified in yeast under
starvation conditions [(6), reviewed in (33)], but we found them to be far more common in
meiosis.
We annotated 10,226 meiotic uORFs, present in the leaders of 3026 genes (fig. S11). These
uORFs contained a density of ribosome footprints far greater than the ribosome footprint
density in non-uORF leader regions, which suggested that our annotation approach was
thorough and specific (fig. S19, A and B). Ribosome occupancy at uORFs was higher in
meiotic than vegetative cells (Fig. 4D and figs. S18A and S19, B and C), and most of this
effect derives from the meiotic program itself rather than the starvation conditions that
accompany sporulation (fig. S19D). As expected, AUG, when present, was efficiently used
for uORF translation initiation. The near-cognate codons that showed most efficient
initiation, UUG and CUG (fig. S18, B and C), have also been shown to be most efficient in
mammalian cells and in vitro (5, 34).
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uORFs have been implicated in translational regulation, although no universal functional
role has emerged. uORFs that have been well-characterized through reporter studies show
diverse effects: enhancing, decreasing, or having little impact on downstream ORF
translation [reviewed in (33)]. Three features of our study ideally positioned us to evaluate
the role of uORFs in translation. First, we annotated many uORFs, which allowed us to
distill general principles. Second, we collected data for each time point on mRNA
abundance and rates of translation, which provided instantaneous quantification of TE for
each downstream ORF, whereas traditional approaches require TE inference by steady-state
protein abundance. Finally, our analysis of numerous sequential points through a dynamic
process permitted us to detect temporal trends. Comparison of ribosome occupancy of
leaders and TE of their corresponding downstream ORFs over 10 time points (see fig. S4)
typically revealed a strong positive correlation (Fig. 4, E and F). However, a subset of
leaders containing at least one AUG uORF showed a negative correlation, which suggested
a competitive relation between uORF and ORF translation in these cases (Fig. 4F and fig.
S20; see Fig. 5E below).
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Leader extensions and competitive uORFs
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For some messages, we found that enriched footprint occupancy of leaders was caused by a
programmed change in the transcript length during meiosis. Systematic analysis identified
192 genes with regulated leader length (Fig. 5, A to C; fig. S21; and table S5). For example,
ORC1 showed an extended leader after prophase. This extension revealed a number of welltranslated uORFs (Fig. 5B) and was accompanied by a concurrent decrease in translation of
the ORC1 coding region (Figs. 3E and 5, B and D). Of genes with regulated leaders, a
prominent subset showed a similar inverse relation, often corresponding well with known
gene function. Orc1 and Ndj1, for example, have no characterized function late in meiosis
(24, 35), and RED1, a key meiotic prophase factor (36), is translationally repressed
exclusively in vegetative cells (Fig. 5D).
For genes with leader extensions containing one or more AUG uORF, at least half showed a
strong negative correlation between the ribosome occupancy of the leader and TE of the
ORF (Fig. 5E). By contrast, for leaders containing uORFs starting only with near-cognate,
non-AUG codons, this correlation was strongly positive (Fig. 5E). Regulated leaders have
been observed in budding yeast and mammalian cells, with longer forms often associated
with poor ORF translation (37, 38). Here we have observed a far broader and more nuanced
role for leader extensions in providing temporal translational control to many meiotic genes.
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More generally, our analyses point to disparate roles for AUG and near-cognate uORFs
(Figs. 4F and 5E). A fraction of AUG uORFs appear to competitively down-modulate ORF
expression. By contrast, near-cognate uORFs are more common and show a generally strong
positive correlation with expression of their downstream ORF, which may allow cells to
divert limited resources to an important subset of messages. Whether uORFs directly prime
translation of their downstream ORF is unclear. Nonetheless, genes with the strongest
positive correlation between leader ribosome occupancy and ORF TE are highly enriched
for known function in sporulation (table S6), which suggests physiological relevance of this
regulation. The broad monitoring of gene expression by genomics has underscored the
importance of quantitative modulation, beyond a model of binary on-off control.
MicroRNAs provide a prominent example of developmental control through subtle
regulation of broad sets of genes. uORFs may similarly allow condition-specific tuning of
protein synthesis for a large portion of the genome.
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The preponderance of uORFs suggests a shift of the translation initiation mechanism in
meiotic cells from the predominant mechanism in which the initiation factors recognize the
mRNA cap and the initiation complex scans the message for the first AUG to commence
translation [reviewed in (23)]. A link between alternative translation initiation mechanisms
and the use of uORFs is suggested by analysis of messages that were shown to support capindependent translation in nitrogen-starved yeast cells (YMR181C, GPR1, BOI1, FLO8,
NCE102, MSN1, and GIC1) (39). We found that all had leaders with well-translated nearcognate uORFs and a strong positive correlation between leader ribosome occupancy and
ORF translation (Fig. 4F; fig. S22, excluding BOI1 as it has a complicating leader extension;
table S5).
Perspective
We find that even in the extensively studied yeast, S. cerevisiae, genome coding has a
complexity not captured by existing annotations. Ribosome profiling also captured a layer of
regulation that is invisible to mRNA measurements and which revealed extensive and
dynamic translational regulation of canonical ORFs. Transcription studies have enabled the
identification of cis- and trans- transcriptional elements that control diverse cellular
processes, whereas a similarly broad understanding of the importance and mechanisms of
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Brar et al.
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translational control remains elusive. This data set provides a valuable foundation for
identifying such cis- and trans-translational regulators.
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This study also gives a holistic view of the metabolic and cellular reorganization, seen
through the yeast meiotic program that extends beyond a traditional chromosome-centric
picture. Previous studies suggested that meiotic transcriptional control was limited to a few
discrete waves (3, 40). Our data reveal multifaceted control of protein production, enabled
by the tightly timed induction of many translational and transcriptional programs, including
those driving translation factors, the proteasome, and the UPR. Indeed, the view of such
responses as environmentally controlled stress pathways may reflect the historical context of
their discovery rather than their sole physiological role.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
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We thank E. Ünal, A. Amon, and members of the Weissman laboratory for critical reading of this manuscript; C.
Chu for sequencing assistance; J. Dunn for protocol development assistance; F. Van Werven for the MATa/a strain;
and F. Klein for the the Zip1 antibody. G.A.B. is supported by American Cancer Society Postdoctoral Fellowship
117945-PF-09-136-01-RMC. M.Y. is supported by a Clore Fellowship. This work was supported by a grant from
the U.S.-Israel Bi-national Science Foundation (N.F. and A.R.), an NIH P01 award (AG10770; N.T.I.), a Ruth L.
Kirschstein National Research Service Award (GM080853; N.T.I.), and Howard Hughes Medical Institute funding
(J.S.W. and A.R.). A patent on the ribosome profiling approach has been assigned to the University of California.
Supporting files are in the SOM and at www.ncbi.nlm.nih.gov/geo/ with series accession number GSE34082.
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Fig. 1.
Ribosome profiling through meiosis. (A) Time points (white lines) were taken through two
overlapping time courses. Cartoon representations of meiotic stages are below. (B) A subset
of staging controls. Positions of staging plots correspond to time points in (A). (C)
Ribosome footprints across specific genes are shown for categories in fig. S4. Scales on the
y axis are independent by gene.
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Fig. 2.
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A global view of protein synthesis through sporulation. (A) Ribosome footprints (RPKMs)
were summed over each yeast gene (columns) for all samples except steady-state spores
(rows). The summed expression of each gene over time points was normalized for the time
course, and genes were subjected to clustering. Several clusters are noted by lines below the
chart: 1, Mitochondrial ribosome; 2, nutrient uptake and/or amino acid biosynthesis; 3,
mitochondrial function; 4, proteasome; 5, redox and energy generation; and 6, ribosome and
translation machinery. Meiotic progression is indicated pictorially to the right. (Top) A
cluster containing genes responsible for DNA replication, with the gene identities to the
right. To the left is the average footprint density across the cluster, with time points
corresponding to bulk DNA replication represented by arrows. (Bottom) A cluster of genes
associated with recombination and SC formation. The bar to the left shows the timing of
these events as determined by staging controls. Single asterisks identify genes analyzed in
(B) and (C) and fig. S7. Double asterisks indicate that both MND1 and the overlapping
ORF, YGL182C, were identified in this cluster. (B) Wild-type, ydr506cΔ, and ydr506c
spo11Δ cells were induced to sporulate. At indicated times, samples were scored for nuclear
division. (C) Wild-type, ylr445wΔ, and ylr445wΔ spo11Δ cells were induced to sporulate
and were treated as in (B).
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Fig. 3.
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Widespread dynamic translational control in meiosis. (A) Log2 mRNA and footprints
(RPKM) for a region containing SPS1 and SPS2 over pooled time points (fig. S4). (B)
SPS1-3HA and SPS2-FLAG cells carrying an estrogen-inducible NDT80 allele were
induced to sporulate. At 6 hours, β-estradiol was added. Samples from indicated times were
subjected to Western blotting (WB) and Northern blotting (NB). (C) Log2 TE values for
CLB3 and YPT1 for pooled time points (fig. S4). MI and MII are indicated by colored
shading as boxes. (D) Cluster analysis of log2 TE through meiosis for pooled categories (fig.
S4) for all genes. (E) Log2 TEs are plotted as in (C) for AMA1, RCR1, ORC1, and ZIP1.
(F) Log2 TEs are plotted as in (C) for HAC1. Below, total RNA from the original time
course (see fig. S1) was subjected to Northern blotting for HAC1.
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Fig. 4.
Noncanonical translation is pervasive in meiotic cells. (A) Footprints from pooled time
points (see fig. S4) were mapped. The percentage of these footprints outside of known ORF
annotations is plotted. (B) mRNA and ribosome occupancy profiles around YOL092W, with
sense above the line for each time point and antisense below. Single asterisk denotes the
sORF start site. The “AUG unit” (sORF) was annotated by the strategy shown in fig. S11.
(C) The region around SAS4 is displayed as in (B), with truncated ORF start denoted by a
single asterisk. (D) Ribosome occupancy profile for vegetative cells in exponential growth
phase and meiotic cells over the leader of ACB1. (E) For pooled time points (see fig. S4),
TEs are plotted for ORFs and for leaders (see SOM for a discussion of leader TE
determination) for IME1, CDC28, and PDS1. Values are normalized to the same range for
both plots. (F) Correlation coefficients [determined from plots as in (E)] were determined
for each gene with uORFs for leaders with only near-cognate uORFs and at least one AUG
uORF. The positions of six genes that support cap-independent translation (39) are noted.
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Fig. 5.
Regulated transcript extensions expose novel regulatory uORFs. (A) mRNA and ribosome
occupancy profiles around SUP35. (B) ORC1 region displayed as in (A). (C) Total RNA
from the original time course (see fig. S1) was subjected to Northern blotting for ORC1. (D)
Analysis as in Fig. 4E for ORC1, SUP35, NDJ1, RED1, NDC80, and POP4. (E) Analysis as
in Fig. 4F for genes with regulated leader extension (table S5).
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